How LLMs can help streamline business processes

Large language models (LLMs) seem ideal for creating natural language interfaces, but the rise of ChatGPT and other similar tools has raised a question: Are LLMs right for optimizing business processes?

In short, the answer is a resounding “yes.” Of course, the longer answer is more nuanced than that. The stand-alone utility or prowess of an LLM is relatively limited unless paired with other technologies. The true impact of AI lies in its ability to facilitate the acceleration of business processes through automation.

Where AI meets business processes

Consider streamlining the “opportunity to order” process within a business. As it stands, regardless of the implemented product or solution, organizations are forced to navigate the complexity of this automation, eventually falling back on manual methods like drag-and-drop interfaces, low-code solutions, or high-code programming.

Now add LLMs to the equation. LLMs are expansive repositories that house knowledge about common processes that are fit for automation. Leveraging this information, the model acts as a catalyst providing a head start to problem-solving. As a result, users don’t need to reinvent the wheel. They only need to customize existing solutions to fit their specific needs and significantly expedite the process.

In this case, the end-user experience remains rooted in natural language interaction. Users could prompt a large language model to build an “opportunity to order” framework tailored to their CRM and ERP using an integration platform as a service (iPaaS). The system could then generate relevant assets to connect and automate the process, allowing users to fine-tune and quickly transition their customized solutions into an operational reality.

In another example, an e-commerce business might use an LLM to create an application that vets incoming orders before integrating them into an ERP system. Traditionally, building such an application would be labor-intensive, whether you coded it from scratch or used a low-code platform. LLMs have revolutionized this approach by being able to interpret the specific requirements and generate an app based on the user’s request.

Many in the agriculture industry are looking for similar optimization. A farmer who has invested in technology but lacks technical sophistication could design an application to monitor their carbon footprint across seasons. In this case, the LLM would use its understanding of carbon tracking needs and data representation to generate a tailor-made application.

These are just a few ways LLMs can reshape processes by automating substantial portions of complex tasks. Because they are able to gain a full understanding of intricate business needs, they can generate custom business solutions. The use cases for LLMs are endless and transcend industry boundaries.

Steps to getting started with LLMs

The rapid evolution and adoption of generative AI technologies indicate that businesses must consider how they can take advantage to remain competitive in their market. For organizations interested in getting started, there are a handful of initial steps they should consider:

  1. Educate yourself. When leveraging any new technology to optimize business processes, preparation is key. This means that educating yourself about the fast-moving landscape of LLMs is pivotal. OpenAI has pioneered the space, commoditizing generative AI with ChatGPT and its various GPT models. But major players including AWS, Google, Meta, and Microsoft—and even emerging entities like Hugging Face—are swiftly launching their own iterations to broaden the spectrum of accessible LLMs for application development.
  2. Get to know the major players. To navigate this expansive field, businesses need to familiarize themselves with the diverse range of vendors and identify the LLM best suited for their specific requirements. This includes exploring offerings from the aforementioned providers and many others to make an informed choice for integration and subsequent application development.
  3. Exercise caution. Given the acceleration of AI advancements, exercising caution is a must. Businesses must monitor the actions of AI models with vigilance to ensure alignment with their intended functions and values, and implement necessary robust security measures.

Ultimately, the rapid evolution of AI emphasizes the need for businesses not only to leverage these advancements but to do so mindfully, selecting the tools that best align with their objectives and security requirements. Nobody wants to be left behind when it comes to adopting new technology for fear of falling behind the competition. But it’s just as important to evaluate the risks and avoid investing in initiatives that eventually fall flat or cause unintended consequences.

Organizations that find a way to strike a balance between early adoption and measured caution put themselves in the best position for long-term success.

Manoj Chaudhary is CTO and SVP of engineering at Jitterbit.

Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Contact

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